Yes, I realize the science these days is super collaborative and needs expensive tools, models and techniques to be cool.

However.

Strategically as a lab, you need to have a bread and butter data stream that you produce in house. Data that you generate, interpret, understand and publish without the input of any other lab groups. Data that is, in and of itself, capable of generating publications that meet at least the lower bound expectations of your department, subfield and whomever else is evaluating you.

This may not be the same thing over the long haul, either. Interests change. But the thing that never changes is that nobody is going to find your publication goals, demands or needs as critical as you do. And in this game, not publishing is simply not an option.

What about some other data stream? I mean lord knows I understand the mental block of "but we do this kind of science in my lab!". all too well. But sometimes you have to think a little more strategically, hard as it may be.

Yeah I would would be in a screaming panic if i worked myself into a situation where my beard and butter data generators were outside my control. I grown to hate working with core facilities too, for the most part.

Well, when all of your training and funded work revolves around the behaviors of different cell types and how they may relate to pathologic/disease states, it's difficult to get around. I do a lot of molecular/cell biology and need to evaluate cellular phenotypes.

Wise words. Another tricky thing is to figure out how to change that data stream mid, uh, stream. There's so much inertia to stick with the bread and butter, but stagnation is death also. I really admire scientists who can flip on a dime. Often seems to require new, talented people joining the lab.

The part about how crummy it is when you're spending all your time grant writing to no avail, and your lab is still so new that nobody in it is getting any data, and you fear that this may never change, and your calendar is getting chewed up by all kinds of departmental responsibilities that prevent you from getting to the bench for anything more than 3 half-assed hours.

I got my PhD in a lab that never had such a stream. We did theory (including the kind that gets tested, but with data that were mostly publicly available). We did well for ourselves. My post doc was in a lab that had some in-house data generation, but I did theory that again using publicly available observations. I'm now wishing I had more background in some kind of data generation, but I can't even decide what kind... my work draws from too many sources, bench and field. But I am feeling acutely the nervousness that comes from not being sure my new collaborators will prioritize what I know would be the killer experiments that I need for big publications. I know of some computer-based scientists in my field who got funding for field work they were formally never trained in, but I still don't get how they pulled it off... and a shift to in-house bench work seems out of the question. Is there really no place for my kind of lab?

"Well, when all of your training and funded work revolves around the behaviors of different cell types and how they may relate to pathologic/disease states, it's difficult to get around. I do a lot of molecular/cell biology and need to evaluate cellular phenotypes."
-It might be worth taking a look at getting yourself some programmers. I'm seeing a lot of papers these days that are 3/4 bioinformatics and then some simple cell culture/animal experiments to back them up. Depending on your university, some of the undergrads/grads can be remarkably competent with that sort of stuff. Even if not, it doesn't take that long for a dedicated person to pick up on (send them to a friendly lab for training, if possible). You just have to get someone to collaborate with who can evaluate the statistical tests and make sure they're the proper ones.
You can either make use of published data to generate new hypotheses (e.g. meta-analysis followed by testing) or you can really milk your big datasets (e.g. same RNA-seq data can provide the hypotheses for a couple papers).
The best thing is, if you have any sort of proficiency with programming, you get the code/workflow from them, and you can learn to run it yourself pretty easily.

If you are known as a selfish douche who avoids committee service and allows other people to do the necessary dirty work that keep the institutional wheels turning, then if you ever find yourself in need of help or dispensation, you will be left in the cold by your colleagues. Academics like to mock administrative service and administrators, but this is mostly selfish juvenile pouting.

As a currently topical analogy, you don't want to be like Ted Cruz. If his colleagues on Capitol Hill encountered him in the Senate lobby on fire, they wouldn't even piss on him.

I second what Comrade says. Being a dickhead doesn't help you in science just as it doesn't help you in the rest of society.

Back to the topic of DM's post...

I always tell people that running a lab is just the same as running a small business. You've got to have a product. If you don't have a product, or don't have a product that anyone wants, then you're going to be out of business. It's pretty simple, really.

I guess there are 2 types of bread-and-butter data streams to consider:

(a) The kind that's directly answering the specific aims of the grant, and will go in the progress report, and form the prelim' data for the renewal. A couple of rick solid methods, preferably done by long-term lab folks that you can trust to just do it right. For us, this stuff has always been in-house, with very little use of core facilities or outside collaborators.

(b) The kind where your lab is well known for a method, and you can guarantee 4-5 middle author papers a year just by doing a few experiments and providing a figure for someone. The shit that gets you 5% salary coverage one someone else's proposal is really useful.

Obviously without (a) there is no (b)m, but (b) is important too. Sure, (a) is what matters when reviewers are looking at the biosketch, but (b) is a big part in padding out those PubMed hits and getting your h-index up. (b) is also great for trainees, for whom a middle author pub every year can help their CV if their own project is progressing slowly.

It seems the folks DM is referring to here, are those with too much emphasis on (b) - too many fingers in other people's projects such that their own science pipeline stalls. However, I think it's important to recognize you can have too much (a) game also. You can have lots of senior authored papers and a great in-house data stream, but if you're not collaborative and all your papers are the same 3 grad students and post-doc's for 5 years in a row, that looks bad. If you have a good data stream, you have to share the goodness, or people will think you're an ass.

I have some limited background in programming and actually use some Linux-based code now. I have thought about taking this avenue, but I wouldn't want to make it my focus since I don't see big payoff for many that do it unless that is your expertise and focus of your lab. It just doesn't get you very far in the kind of work I am doing now. That being said, the bioinformatics angle is a real possibility. I actually have some RNAseq stuff in one of my grants that I am hoping to get done later this year.

The idea that non-programmers can take a few online courses, and then start churning out impactful bioinfo papers is nonsense. This massively underestimates the training involved in doing this kind of work. Full time professional bio-info people use a full suite of platforms to achieve excellent results, including python, R, perl, C, etc. If you can't use R to it's full potential, you wont be able to compete.

If one wants to run basic stuff like RNA-Seq, I would suggest trying out Galaxy on a cloud service like Amazon. It's really not that bad these days.

@Emaderton. It really is dependent on the type of work you do, but it can be a good jumping off point, or a way to broaden the applications of your findings at the end of a paper (and generate new leads).

@Dave
Of course. Real bioinformatics requires lots of skills and qc and best practices practical knowledge that takes a long time to build. But I have 3 stories going that started with a bunch of other people's expression profiling data, and a basic knowledge of how to work with csvs and use the intersect function in R.

Ola - In my experience, productivity is productivity. The real issue is lead author (first/last*) over middle authorships. Middle authorships will add to your total, but (in my experience) are generally not enough. I like to say if you have 5 middle author papers, you've got nothing. But if you have 5 middle author papers and 5 first/last author papers, you've got 10 papers.

* first author when you are young, last author when it's your lab. (Or the equivalent in your field. Each field has a different authorship meaning. By "first", I mean student lead. By "last", I mean lab lead.)